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Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 153 - 168 Titre : Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa Type de document : Article/Communication Auteurs : Cecilia Masemola, Auteur ; Moses Azong Cho, Auteur ; Abel Ramoelo, Auteur Année de publication : 2020 Article en page(s) : pp 153 - 168 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Acacia (genre) [Termes descripteurs IGN] Afrique du sud (état) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] cartographie automatique [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] espèce exotique envahissante [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) Invasive alien plants (IAPs) threaten biodiversity and critical ecosystem services worldwide. There is, therefore, an urgent need to develop intervention measures to control the spread of IAPs. Efforts to control and monitor the spread of IAPs would require their current and detailed distribution over a large geographic area. Recently launched multispectral instrument on-board Sentinel-2 provides free data with good spatiotemporal and spectral resolution, compared to Landsat datasets. The Sentinel-2 dataset, therefore, can be a useful source of the IAPs spatial information required for detection and monitoring purposes. We combined Sentinel-2 data with a radiative transfer model to discriminate IAPs (Acacia mearnsii and Acacia dealbata) from surrounding native tree species in Van Reenen, KwaZulu-Natal, South Africa. The forward mode of combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL was used to simulate reflectance corresponding to bands of Sentinel-MSI, while the PROSAIL model inversion retrieved leaf area index (LAI) and canopy chlorophyll contents (CCC) of the IAPs and native species. Both reflectance and retrieved properties were used to map the distribution of the species within the study area. Our results showed that A. mearnsii and A. dealbata could be accurately discriminated from the surrounding native trees using integrated PROSAIL Sentinel-2 based model. We found that CCC– and LAI-based (% accuracy = 92.8%, 91.4% for CCC and LAI, respectively) modelling produced a higher classification accuracy than field sampling-based modelling (Accuracy = 90.2% (IAP), 82.2% (NAT) and kappa coefficient = 0.84 (IAP), 0.78 (NAT)). Simulated bands corresponding to Sentinel-2 data, on the other hand, produced species maps comparable to field sampling-based maps. Overall, the integrated PROSAIL Sentinel-2 inversion approach proved suitable for detecting and mapping IAPs over a large area. Due to the high spatiotemporal coverage of Sentinel-2, satellite images, the model developed showed the potential to contribute to the IAPs monitoring systems. Numéro de notice : A2020-352 Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.009 date de publication en ligne : 13/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95235 [article]

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The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 161 - 167 Titre : The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study Type de document : Article/Communication Auteurs : Yanan Yan, Auteur ; Lei Deng, Auteur ; L. Xian-Lin, Auteur Année de publication : 2020 Article en page(s) : pp 161 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] agrégation spatiale [Termes descripteurs IGN] anisotropie [Termes descripteurs IGN] approche pixel [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] dispersion [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] télédétection Résumé : (auteur) Spectral decomposition of mixed pixels can provide information about the abundance of end members but fails to indicate the spatial distribution of end members in vegetation remote sensing. This work is a significant attempt to use the bidirectional reflectance distribution function (BRDF) characteristics of mixed pixels in the prediction of spatial-heterogeneity metrics. Data sets from this function with different spatial distributions were constructed by the discrete anisotropic radiative transfer model, and three spatial aggregation and dispersion metrics were calculated: percentage of like adjacencies, spatial division index, and aggregation index. A simple linear regression method was used to construct the prediction model of spatial aggregation and dispersion metrics. The potential of multiangle remote sensing model for identifying spatial patterns well was demonstrated, and its importance was found to differ for different spatial aggregation and dispersion metrics. Specifically, the precision of the model based on multiangle reflectance used for predicting the spatial division index could meet a minimum root mean square of 5.95%. The reflectance features from backward observation on the principal plane play the leading role in recognizing the spatial heterogeneity of mixed pixels. The prediction model is sufficiently robust to distinguish the same vegetation with different growth trends, but also performs well when the ground objects have a smaller reflectance difference in the mixed pixels in a certain band. This study is expected to offer a new thought for spatial-heterogeneity identification of ground objects and thus promote the development of remote sensing technology in assessing spatial distribution. Numéro de notice : A2020-146 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.161 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.161 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94775 [article]

Quantification of the adjacency effect on measurements in the thermal infrared region / Xiaopo Zheng in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9674 - 9687 Titre : Quantification of the adjacency effect on measurements in the thermal infrared region Type de document : Article/Communication Auteurs : Xiaopo Zheng, Auteur ; Zhao-Liang Li, Auteur ; Xia Zhang, Auteur ; Guofei Shang, Auteur Année de publication : 2019 Article en page(s) : pp 9674 - 9687 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes descripteurs IGN] adjacence [Termes descripteurs IGN] exitance spectrale [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectivité [Termes descripteurs IGN] température au sol Résumé : (auteur) Sensor-observed energy from adjacent pixels, known as the adjacency effect, influences land surface reflectivity retrieval accuracy in optical remote sensing. As the spatial resolution of thermal infrared (TIR) images increases, the adjacency effect may influence land surface temperature (LST) retrieval accuracy in TIR remote sensing. However, to our knowledge, few studies have focused on quantifying this adjacency effect on TIR measurements. In this study, a forward adjacency effect radiative transfer model (FAERTM) was developed to quantify the adjacency effect on high-spatial-resolution TIR measurements. The model was verified to be in good agreement with moderate resolution atmospheric transmission (MODTRAN) code, with a discrepancy 3 K in some cases. These findings indicate that the adjacency effect should be considered when retrieving LSTs from TIR measurements, at least in some specific conditions. The proposed FAERTM provides a useful model for quantifying and addressing the adjacency effect on TIR measurements Numéro de notice : A2019-600 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2928525 date de publication en ligne : 06/08/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2928525 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94599 [article]

Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data / Abdelhakim Amazirh in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 11 - 26 Titre : Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data Type de document : Article/Communication Auteurs : Abdelhakim Amazirh, Auteur ; Olivier Merlin, Auteur ; Salah Er-Raki, Auteur Année de publication : 2019 Article en page(s) : pp 11 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] désagrégation [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] Maroc [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] zone semi-aride Résumé : (Auteur) The use of land surface temperature (LST) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. Unfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. MODIS: Moderate Resolution Imaging Spectro-radiometer) or high spatial (e.g. Landsat) resolution separately. Disaggregating low spatial resolution (LR) LST data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution (HR) LST observations. Existing LST downscaling approaches generally rely on the fractional green vegetation cover (fgv) derived from HR reflectances but they do not take into account the soil water availability to explain the spatial variability in LST at HR. In this context, a new method is developed to disaggregate kilometric MODIS LST at 100 m resolution by including the Sentinel-1 (S-1) backscatter, which is indirectly linked to surface soil moisture, in addition to the Landsat-7 and Landsat-8 (L-7 & L-8) reflectances. The approach is tested over two different sites – an 8 km by 8 km irrigated crop area named “R3” and a 12 km by 12 km rainfed area named “Sidi Rahal” in central Morocco (Marrakech) – on the seven dates when S-1, and L-7 or L-8 acquisitions coincide with a one-day precision during the 2015–2016 growing season. The downscaling methods are applied to the 1 km resolution MODIS-Terra LST data, and their performance is assessed by comparing the 100 m disaggregated LST to Landsat LST in three cases: no disaggregation, disaggregation using Landsat fgv only, disaggregation using both Landsat fgv and S-1 backscatter. When including fgv only in the disaggregation procedure, the mean root mean square error in LST decreases from 4.20 to 3.60 °C and the mean correlation coefficient (R) increases from 0.45 to 0.69 compared to the non-disaggregated case within R3. The new methodology including the S-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35 °C and a mean R increasing to 0.75. Numéro de notice : A2019-136 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.004 date de publication en ligne : 15/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92467 [article]

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Monitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)  

Public Titre : Monitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations Type de document : Thèse/HDR Auteurs : Abdelhakim Amazirh, Auteur ; A. Chehbouni, Directeur de thèse ; Salah Er-Raki, Auteur Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2019 Importance : 240 p. Format : 21 x 30 cm Note générale : bibliographie Thèse en vue de l’obtention du Doctorat de l'Université de Toulouse, Spécialité : Surfaces et Interfaces Continentales, Hydrologie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] gestion de l'eau [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] MARRAKECH [Termes descripteurs IGN] micro-onde [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] parcelle agricole [Termes descripteurs IGN] ressources en eau [Termes descripteurs IGN] stress hydrique [Termes descripteurs IGN] température de surface [Termes descripteurs IGN] zone semi-aride Résumé : (auteur) Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm. Note de contenu : General Introduction 1- Bibliographic synthesis 2- Data & study sites description 3- Models & methods 4- Results & discussions Conclusions and perspectives Numéro de notice : 25694 Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Surfaces et Interfaces Continentales, Hydrologie : Toulouse : 2019 Organisme de stage : CESBIO - Centre d'Etudes Spatiales de la Biosphère En ligne : http://thesesups.ups-tlse.fr/4412/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94759

ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery / Pablo J. Zarco-Tejada in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)  

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The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas / Emanuele Santi in Remote sensing of environment, vol 200 (October 2017)  

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Retrieving grassland canopy water content by considering the information from neighboring pixels / Binbin He in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)  

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Simultaneous estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from multiple-satellite data / Han Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Télédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)

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Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework / H. Croft in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

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On the SAR backscatter of burned forests: a model-based study in C-Band, over burned pine canopies full text / Vasileios kalogirou in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)

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Laboratory measurements of plant drying: Implications to estimate moisture content from radiative transfer models in two temperate species / Sara Jurdao in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)  

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Improvement and application of the conifer forest multiangular hybrid GORT model MGeoSAIL / Qiang Wang in IEEE Transactions on geoscience and remote sensing, vol 51 n° 10 (October 2013)  

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